Grants and Contributions

May 2024 - This project seeks to support ALNAP’s 2024-2026 workplan and contribute to the promotion of lessons learned and the dissemination of best practices across the humanitarian sector.

To overcome this limitation, a surrogate evaluation function or a "surrogate model", is often used to replace the evaluation process and a learning approach is taken to estimate the parameters of the model. More specifically, the surrogate model is learned from a set of designs for which quality measurements have been obtained from the original evaluation process. This Project focuses on studying the effectiveness and robustness of such a learning approach and developing novel methods for this purpose.

P006358001: The project aims to improve learning for pre-primary, primary-aged and out-of-school girls and boys in the Cox's Bazar district of Bangladesh through gender equitable quality education.

These mirrors are manufactured on high precision single point diamond Computer Numerical Control (CNC) optical fabrication machines. This project will develop higher precision metal mirrors for these applications and will include development of new on machine metrology, analysis, and control capabilities to allow for the manufacture of both ultra high performance telescopes for science missions and high volume production of similar performance Low Earth Orbit (LEO) communication satellite telescopes.

A Digital Twin can be used to proactively identify potential issues with its real physical counterpart, which
allows the prediction of the remaining useful life of the physical twin by deploying machine learning-based models (or so-called data-driven models) developed from “big data”.

We propose to combine the hyperspectral imaging tools of coherent Raman microscopy, Surface Enhanced Raman Scattering (SERS) plasmonic sensors, and advanced machine learning for the rapid screening of CMs with respect to their preg-robbing ability.

The Project is a collaboration between Carnegie Mellon University, the University of Toronto, and the National Research Council to develop and apply data-driven and machine learning methods for the experimental discovery of new acid-stable materials promoting the oxygen evolution reaction.

The Project combines advanced experimental techniques, density functional theory (DFT) and machine learning (ML) to develop and identify active, stable, and selective catalyst materials for CO2 reduction reaction (CO2RR).